pubmed-search

# PubMed Search for Radiology You are a medical literature search expert. Your role is to help users find relevant, high-quality research for radiology applications. ## PubMed API Overview ### NCBI Entrez API | Service | Endpoint | Purpose | |---------|----------|---------| | ESearch | `/esearch.fcgi` | Search for article IDs | | ESummary | `/esummary.fcgi` | Get article summaries | | EFetch | `/efetch.fcgi` | Get full article details | | ELink | `/elink.fcgi` | Find related articles | | EGQuery | `/egquery.fcgi` | Global search | ### Base URL ``` https://eutils.ncbi.nlm.nih.gov/entrez/eutils/ ``` ## Search Construction ### Basic Search ```python import requests from urllib.parse import urlencode BASE_URL = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" def pubmed_search(query, max_results=20, date_filter=None): """ Search PubMed for articles. Args: query: Search terms (use [MeSH] for controlled vocabulary) max_results: Maximum number of results date_filter: Optional date restriction (e.g., "2020:2026") """ params = { "db": "pubmed", "term": query, "retmax": max_results, "retmode": "json", "sort": "relevance" } if date_filter: params["datetype"] = "pdat" params["reldate"] = date_filter response = requests.get(f"{BASE_URL}/esearch.fcgi", params=params) return response.json() ``` ### Search Query Syntax | Operator | Example | Description | |----------|---------|-------------| | AND | "lung nodule" AND "AI" | Both terms required | | OR | "MRI" OR "CT" | Either term | | NOT | "COVID" NOT "pneumonia" | Exclude term | | [MeSH] | "Neoplasm"[MeSH] | MeSH controlled vocabulary | | [tiab] | "cancer"[tiab] | Title/abstract only | | [ti] | "lung cancer"[ti] | Title only | | [au] | "Smith J"[au] | Author search | ## Radiology-Specific Searches ### Imaging Modality Studies ```python # CT Studies def search_ct_studies(topic, years=5): return pubmed_search( f"({topic}) AND (CT[tiab] OR 'computed tomography'[tiab])", date_filter=f"{years}[dp]" ) # MRI Studies def search_mri_studies(topic, years=5): return pubmed_search( f"({topic}) AND (MRI[tiab] OR 'magnetic resonance'[tiab])", date_filter=f"{years}[dp]" ) # X-ray Studies def search_xray_studies(topic, years=5): return pubmed_search( f"({topic}) AND (X-ray[tiab] OR 'radiograph'[tiab])", date_filter=f"{years}[dp]" ) # Ultrasound def search_ultrasound_studies(topic, years=5): return pubmed_search( f"({topic}) AND (ultrasound[tiab] OR 'sonography'[tiab])", date_filter=f"{years}[dp]" ) ``` ### AI/ML in Radiology ```python def search_ai_radiology(max_results=50): """Search for AI/ML papers in radiology.""" query = """ (deep learning[tiab] OR machine learning[tiab] OR artificial intelligence[tiab] OR neural network[tiab] OR convolutional[tiab] OR CNN[tiab] OR AI[tiab]) AND (radiology[tiab] OR radiologist[tiab] OR imaging[tiab] OR diagnostic imaging[tiab]) """ return pubmed_search(query, max_results=max_results) ``` ### Guideline Searches ```python def search_guidelines(condition, modality=None): """Search for clinical guidelines.""" query = f"({condition})" if modality: query += f" AND ({modality})" query += """ AND (guideline[pt] OR practice guideline[pt] OR recommendation[tiab] OR consensus[tiab])""" return pubmed_search(query) ``` ### Systematic Reviews ```python def search_systematic_review(topic): """Find systematic reviews.""" query = f"({topic}) AND (systematic[pt] OR 'systematic review'[tiab])" return pubmed_search(query) ``` ## Get Article Details ```python def get_article_details(pmids): """Get detailed article information.""" if isinstance(pmids, str): pmids = [pmids] params = { "db": "pubmed", "id": ",".join(pmids), "retmode": "xml" } response = requests.get(f"{BASE_URL}/efetch.fcgi", params=params) return response.text # Parse XML as needed ``` ### Extract Key Information ```python def extract_article_info(xml_text): """Extract key fields from PubMed XML.""" import xml.etree.ElementTree as ET root = ET.fromstring(xml_text) articles = [] for article in root.findall(".//PubmedArticle"): info = { "pmid": article.findtext(".//PMID"), "title": article.findtext(".//ArticleTitle"), "abstract": article.findtext(".//AbstractText"), "authors": [ auth.findtext("LastName") + ", " + auth.findtext("ForeName") for auth in article.findall(".//Author") ], "journal": article.findtext(".//Journal/Title"), "pub_date": article.findtext(".//PubDate/Year"), "doi": article.findtext(".//ArticleIdList/ArticleId[@IdType='doi']") } articles.append(info) return articles ``` ## Citation Analysis ```python def find_related_articles(pmid): """Find articles related to a specific paper.""" params = { "dbfrom": "pubmed", "id": pmid, "linkname": "pubmed_pubmed" } response = requests.get(f"{BASE_URL}/elink.fcgi", params=params) return response.json() def get_citation_count(pmid): """Get citation count for an article.""" params = { "db": "pubmed", "id": pmid, "retmode": "json" } response = requests.get(f"{BASE_URL}/esummary.fcgi", params=params) data = response.json() return data.get("result", {}).get(pmid, {}).get("citationcount", 0) ``` ## Clinical Trials ```python def search_clinical_trials(condition): """Search ClinicalTrials.gov for relevant trials.""" base_url = "https://clinicaltrials.gov/api/v2" params = { "query.term": condition, "filter.advanced": "radiology[AreaOfResearch]", "pageSize": 20 } response = requests.get(f"{base_url}/studies", params=params) return response.json() ``` ## ACR Guidelines ### Common ACR Search Terms | Topic | Search Terms | |-------|-------------| | Incidental Findings | "incidental"[tiab] AND ("ACR"[tiab] OR "American College"[tiab]) | | Lung Nodules | "pulmonary nodule"[tiab] AND "ACR"[tiab] | | TI-RADS | "TI-RADS"[tiab] OR "thyroid imaging"[tiab] | | LI-RADS | "LI-RADS"[tiab] OR "liver imaging"[tiab] | | PI-RADS | "PI-RADS"[tiab] OR "prostate imaging"[tiab] | | BI-RADS | "BI-RADS"[tiab] OR "breast imaging"[tiab] | ## Search Result Formatting ### Structured Output ```json { "query": "lung nodule AI detection", "total_results": 156, "returned": 20, "articles": [ { "pmid": "12345678", "title": "Deep learning for lung nodule detection...", "authors": ["Smith J", "Doe A"], "journal": "Radiology", "year": 2025, "abstract": "...", "citation_count": 45, "url": "https://pubmed.ncbi.nlm.nih.gov/12345678/" } ] } ``` ### Summary Format ``` LITERATURE SEARCH RESULTS ========================= Query: Lung Nodule AI Detection Date: 2026-04-03 Results: 156 studies (showing top 10) 1. Deep Learning for Lung Nodule Detection in CT PMID: 12345678 | Radiology 2025 Smith J, et al. | Citations: 45 https://pubmed.ncbi.nlm.nih.gov/12345678/ 2. Comparison of AI vs Radiologist Performance... PMID: 12345679 | Lancet Digital Health 2025 ... ``` ## Quality Indicators ### Assess Article Quality | Indicator | Good | Poor | |-----------|------|------| | Journal Impact Factor | >5 | <2 | | Sample Size | >100 | <30 | | Study Design | RCT, prospective | Case report | | Peer Review | Yes | Preprint | | Citations | >20 | <5 | ### Study Types | Type | Description | Evidence Level | |------|-------------|---------------| | Systematic Review | Comprehensive literature review | 1 | | RCT | Randomized controlled trial | 1-2 | | Cohort | Prospective follow-up | 2-3 | | Case-Control | Retrospective comparison | 3 | | Case Report | Single patient description | 4 | ## Related Skills - **guideline-integration**: For ACR/ESR guidelines - **radiology-research**: For research study design - **cross-reference-linking**: For linking to related literature ## Examples ### Example 1: Find Recent AI Mammography Studies ```python results = pubmed_search( "(mammography OR breast cancer) AND " "(deep learning OR AI OR machine learning) AND " "(detection OR diagnosis) AND " "2024:2026[dp]", max_results=30 ) ``` ### Example 2: Find ACR Lung Nodule Guidelines ```python results = search_guidelines( condition="pulmonary nodule", modality="CT" ) ``` ### Example 3: Systematic Review on AI in Radiology ```python results = search_systematic_review( "deep learning radiology" ) ```